Utku Norman | EPFL Graph Search

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Utku Norman is a doctoral assistant at CHILI Lab, and a PhD student in Computer Science at EPFL, Switzerland. He is driven by a desire to help build a better future, and understand the world along the way: His chosen course for how is advancing machine intelligence to develop systems that try to understand us. Utku is curious about how humans, unlike robots, come to be so highly skilled in understanding each other, and detecting and addressing misunderstandings. One way humans do so is by representing whether the others understood what we said or did by using the complex cognitive ability of mutual modelling, i.e. the reciprocal ability to construct a mental representation of the other, by attributing beliefs, desires and other mental states to the other. This ability is critical in order for humans to comprehend each other and react appropriately in their interactions. Thus, the main goal of my PhD is to equip a robot with mutual modelling ability, and use this ability in an educational activity in order to improve the quality of the interactions between the robot and a learner and (hopefully) the learning outcomes.

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Robot

A robot is a machine—especially one programmable by a computer—capable of carrying out a complex series of actions automatically. A robot can be guided by an external control device, or the contro

Robotics

Robotics is an interdisciplinary branch of electronics and communication, computer science and engineering. Robotics involves the design, construction, operation, and use of robots. The goal of rob

Collaborative learning

Collaborative learning is a situation in which two or more people learn or attempt to learn something together. Unlike individual learning, people engaged in collaborative learning capitalize on one a

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Computational Models of Mutual Understanding for Human-Robot Collaborative Learning

Utku Norman

There is a growing trend towards designing learning activities featuring robots as collaborative exercises where children work together to achieve the activity objectives, generating interactions that can trigger learning processes. Witnessing such activities allows for making an interesting observation: humans, unlike robots, are highly skilled in detecting and addressing misunderstandings, building a mutual understanding about the task, and converging to a shared solution. A social robot equipped with these abilities can monitor the interaction and contribute to it, promoting and supporting the building of a mutual understanding, which may trigger learning. To verify this hypothesis, in this thesis, we first develop abilities for social robots to assess how humans build a mutual understanding. To this end, we (i) propose automatic measures to reveal structures in how children "align" in their dialogue and actions, when engaging in a collaborative activity aiming to foster their computational thinking skills and (ii) study how these lead to their performance in the task and learning outcomes; on data we collected in a large user study involving 78 children at schools for a collaborative activity we designed that elicits dialogue. Then, we equip the robot with mutual modeling abilities to build a mutual understanding with a learner: we (iii) present a framework for the robot to build and maintain a mental model, with its own beliefs about the activity, the human, as well as the human's beliefs about the robot; and (iv) evaluate the effects of robot behaviors that are guided by different mental models via an experiment with 61 children at schools, in which a child and the robot collaborate on a variant of the problem solving activity used throughout the thesis.

EPFL2023

Efficacy of a ‘Misconceiving’ Robot to Improve Computational Thinking in a Collaborative Problem Solving Activity: A Pilot Study

Barbara Bruno, Alexandra Lara Chin, Pierre Dillenbourg, Utku Norman

Robot-mediated learning activities are often designed as collaborative exercises where children work together to achieve the activity objectives. Although miscommunications and misunderstandings occur frequently, humans, unlike robots, are very good at overcoming them and converging to a shared solution. With the aim of equipping a robot with these abilities and exploring its effects, in this article we investigate how can a humanoid robot collaborate with a human learner to construct a shared solution to a problem via suggesting actions and (dis)agreeing with each other. Concretely, we designed a learning activity aiming to improve the computational thinking skills of children, in which the robot makes suggestions on what to do, that may be in line with what the human thinks or not. Furthermore, the robot may suggest wrong actions that could essentially prevent them from finding a correct solution. Via a pilot study conducted remotely with 9 school children, we investigate whether the interaction results in positive learning outcomes, how does the collaboration evolve, and how these relate to each other. The results show positive learning outcomes for the participants in terms of finding better solutions, suggesting that the collaboration with the robot might have helped trigger the learning mechanisms.

2022

Mutual Modelling Ability for a Humanoid Robot: How can it improve my learning as we solve a problem together?

Barbara Bruno, Pierre Dillenbourg, Utku Norman

2021